Published September 15, 2023 | Version v1
Dataset Open

PHIL-ADVANCE (Alcantara and Ahn, 2024)

  • 1. Kongju National University
  • 2. Kongju National Unvirsity

Description

The high-resolution nationwide daily precipitation dataset spanning 20 years (2001-2020) for the Philippines, known as "PHIL-ADVANCE" and generated by Alcantara and Ahn (2024), presents a valuable resource. This dataset offers daily precipitation information covering the entire country, featuring a spatial resolution of 0.1° x 0.1°. The dataset is created using the Time-Varying Quadruple Collocation (TV-QC) approach, which takes into account the dynamic nature of uncertainties within parent datasets. By leveraging these errors, it combines data from four parent datasets: CHIRPS, GPM, ERA5, and PERSIANN. The outcome is a dataset of heightened accuracy compared to any of the individual parent datasets.

This dataset is conveniently available in NetCDF format (version 4), a widely recognized and standardized data format commonly employed for exchanging large data files. It can be effortlessly read using major programming languages like R, Python, C++, and others, ensuring easy accessibility for various research applications. With its impressive spatial and temporal resolution, "PHIL-ADVANCE" proves invaluable for climate research, hydrological modeling, and related fields. Additionally, there are plans to incorporate data from 2021 onwards, further enriching its utility and relevance.

Files

ReadMe.txt

Files (440.7 MB)

Name Size Download all
md5:8a6dd372dac2782815cd8d117ef58d0b
22.0 MB Download
md5:af8c8878cc789398a3ac91120b0dc9f0
22.0 MB Download
md5:e52262663aeb941ee40151699187560f
22.0 MB Download
md5:c4fd6a89e64418bd1623a67f72d5e002
22.1 MB Download
md5:b1f89765ed1f7371c84ad48743daa293
22.0 MB Download
md5:8cbc0b25249724999b49947848ffc954
22.0 MB Download
md5:f077067cbdf950fee902a0d9ecb760a9
22.0 MB Download
md5:c2df6954575800912d20af22be331b1c
22.1 MB Download
md5:a56a886c9464e3ddc21cbce945d3d4f2
22.0 MB Download
md5:23290084916e39773e74052607aea12d
22.0 MB Download
md5:9aeab2772655d6d4835eb27072c1a0ca
22.0 MB Download
md5:31c6c50d78954f228a3156c9a8f9d8ac
22.1 MB Download
md5:120223249481a0de4f71153a0e0ab260
22.0 MB Download
md5:d2f2e13d0a192cf216883b7a002d9f01
22.0 MB Download
md5:7d743297e0d4b1133841ef5b6e2a8318
22.0 MB Download
md5:cb99e7494e9fd349126424560c9e698c
22.1 MB Download
md5:dd7bd5df09968ed61e3c9e9a04ae70c2
22.0 MB Download
md5:f385086b7d0ad8f693283388b5c2d58c
22.0 MB Download
md5:eb349b80e643a47275a0cdca32668035
22.0 MB Download
md5:b6b53f5a886b8d790e26a07dbb191288
22.1 MB Download
md5:b7b417bccdacaab715dc0c361cfa8b9e
3.7 kB Preview Download

Additional details

References

  • Alcantara, A. L., & Ahn, K. H. (2024). Time-varying quadruple collocation for enhanced satellite and reanalysis precipitation data error estimation and integration. International Journal of Applieed Earth Observations and Geoinformation